import re from abc import ABC, abstractmethod from collections.abc import Callable, ItemsView, Iterable, Mapping, Sequence from dataclasses import dataclass from functools import lru_cache from typing import Any, Generic, NamedTuple, Optional, Protocol, TypeVar, Union import torch from transformers import BatchFeature, ProcessorMixin from typing_extensions import TypeAlias, TypedDict from vllm.inputs import DummyData, InputProcessingContext from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer from vllm.utils import flatten_2d_lists, full_groupby, is_list_of from .inputs import (AudioItem, ImageItem, MultiModalDataDict, MultiModalInputsV2, MultiModalKwargs, PlaceholderRange, VideoItem) def bind_prompt_sequence( seq: Union[str, list[int]], tokenizer: AnyTokenizer, ) -> "_BoundPromptSequence": """ Bind a text or token sequence to a tokenizer so that it can be lazily converted into the other format on demand. """ return _BoundPromptSequence( tokenizer=tokenizer, _text=seq if isinstance(seq, str) else None, _token_ids=seq if isinstance(seq, list) else None, ) _T = TypeVar("_T") _S = TypeVar("_S", str, list[int]) @dataclass class PromptReplacement(Generic[_S, _T]): target: _S """The text or token sequence to find and replace.""" repl_unit: _S """ The unit making up the replacement text or token sequence. See :code:`repl_count` for more details. """ repl_count: Union[Callable[[list[_T], BatchFeature, int], int], int] """ Given the original multi-modal items for this modality, HF-processed data, and index of the processed item, output the number of repetitions of :code:`repl_unit` to build up the replacement text or token sequence. For convenience, you can pass in an integer if the number of repetitions is a constant. """ def __repr__(self) -> str: return (f"{type(self).__name__}(target={self.target!r}, " f"repl_unit={self.repl_unit!r})") def bind( self, modality: str, tokenizer: AnyTokenizer, ) -> "_BoundPromptReplacement[_T]": return _BoundPromptReplacement( modality=modality, target=bind_prompt_sequence(self.target, tokenizer), repl_unit=bind_prompt_sequence(self.repl_unit, tokenizer), repl_count=self.repl_count, ) @dataclass class ModalityProcessingMetadata(Generic[_T]): prompt_repls: Sequence[Union[PromptReplacement[str, _T], PromptReplacement[list[int], _T]]] """ Defines each text or token sequence to replace in the HF-processed prompt. This is skipped if the HF-processed prompt is found to already contain the replacement prompts. """ class MultiModalProcessingMetadataBuiltins(TypedDict, total=False): """Type annotations for modality types predefined by vLLM.""" image: ModalityProcessingMetadata[ImageItem] video: ModalityProcessingMetadata[VideoItem] audio: ModalityProcessingMetadata[AudioItem] MultiModalProcessingMetadata: TypeAlias = \ Mapping[str, ModalityProcessingMetadata[Any]] """ A dictionary containing an entry for each modality type to process. Note: This dictionary also accepts modality keys defined outside :class:`MultiModalProcessingMetadataBuiltins` as long as a customized plugin is registered through the :class:`~vllm.multimodal.MULTIMODAL_REGISTRY`. Read more on that :ref:`here `. """ def _encode( tokenizer: AnyTokenizer, text: str, *, add_special_tokens: bool = False, ) -> list[int]: """ Backend-agnostic equivalent of HF's :code:`tokenizer.encode(text, add_special_tokens=...)`. """ if isinstance(tokenizer, MistralTokenizer): return tokenizer.tokenizer.encode(text, bos=add_special_tokens, eos=add_special_tokens) return tokenizer.encode(text, add_special_tokens=add_special_tokens) @lru_cache(maxsize=2048) def _cached_encode( tokenizer: AnyTokenizer, text: str, *, add_special_tokens: bool = False, ) -> list[int]: return _encode(tokenizer, text, add_special_tokens=add_special_tokens) def _decode( tokenizer: AnyTokenizer, token_ids: list[int], *, skip_special_tokens: bool = False, ) -> str: """ Backend-agnostic equivalent of HF's :code:`tokenizer.decode(token_ids, skip_special_tokens=...)`. """ return tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens) @lru_cache(maxsize=2048) def _cached_decode( tokenizer: AnyTokenizer, token_ids: tuple[int, ...], *, skip_special_tokens: bool = False, ) -> str: return _decode(tokenizer, list(token_ids), skip_special_tokens=skip_special_tokens) class _HasModalityAttr(Protocol): modality: str class _HasModalityProp(Protocol): @property def modality(self) -> str: ... _M = TypeVar("_M", bound=Union[_HasModalityAttr, _HasModalityProp]) def full_groupby_modality(values: Iterable[_M]) -> ItemsView[str, list[_M]]: """Convenience function to apply :func:`full_groupby` based on modality.""" return full_groupby(values, key=lambda x: x.modality) @dataclass class _BoundPromptSequence: tokenizer: AnyTokenizer _text: Optional[str] _token_ids: Optional[list[int]] def __post_init__(self) -> None: if self._text is None and self._token_ids is None: raise ValueError("At least one of 'text' and 'token_ids' must be " "specified") @property def text(self) -> str: if self._text is None: assert self._token_ids is not None self._text = _cached_decode(self.tokenizer, tuple(self._token_ids)) return self._text @property def token_ids(self) -> list[int]: if self._token_ids is None: assert self._text is not None self._token_ids = _cached_encode(self.tokenizer, self._text) return self._token_ids def __repr__(self) -> str: return (f"{type(self).__name__}(_text={self._text!r}, " f"_token_ids={self._token_ids!r})") @dataclass class _BoundPromptReplacement(Generic[_T]): modality: str target: _BoundPromptSequence repl_unit: _BoundPromptSequence repl_count: Union[Callable[[list[_T], BatchFeature, int], int], int] def get_count( self, mm_items: list[_T], hf_inputs: BatchFeature, item_idx: int, ) -> int: repl_count = self.repl_count if isinstance(repl_count, int): return repl_count return repl_count(mm_items, hf_inputs, item_idx) def to_multi_format(data: MultiModalDataDict) -> dict[str, list[Any]]: """ Convert a :class:`MultiModalDataDict` containing single data items to a :class:`MultiModalMultiDataDict` containing multiple data items per entry. """ multi_data = dict[str, list[Any]]() for k, v in data.items(): # yapf: disable if k == "video": # Special case since even a single item can be a list multi_data[k] = v if is_list_of(v, list) else [v] # type: ignore[index] elif k in ("image", "audio"): multi_data[k] = v if isinstance(v, list) else [v] # type: ignore[index] else: multi_data[k] = v if isinstance(v, list) else [v] # type: ignore[index] # yapf: enable return multi_data class _TokenMatch(NamedTuple): start_idx: int end_idx: int def iter_token_matches( token_ids: list[int], match_ids: list[int], ) -> Iterable[_TokenMatch]: """Yield each occurrence of :code:`match_ids` in :code:`token_ids`.""" match_len = len(match_ids) last_end_idx = 0 for start_idx in range(len(token_ids) - match_len + 1): if start_idx < last_end_idx: continue # Exclude overlapping matches end_idx = start_idx + match_len if token_ids[start_idx:end_idx] == match_ids: yield _TokenMatch(start_idx=start_idx, end_idx=end_idx) last_end_idx = end_idx class _PromptReplacementMatch(ABC, Generic[_T, _S]): prompt_repl: _BoundPromptReplacement[_T] @property def modality(self) -> str: return self.prompt_repl.modality @property @abstractmethod def start_idx(self) -> int: raise NotImplementedError @property @abstractmethod def end_idx(self) -> int: raise NotImplementedError @property @abstractmethod def repl_unit(self) -> _S: raise NotImplementedError def __repr__(self) -> str: return (f"{type(self).__name__}(modality={self.modality!r}, " f"start_idx={self.start_idx!r}, end_idx={self.end_idx!r})") @dataclass(repr=False) class _PromptReplacementTokenMatch(_PromptReplacementMatch[_T, list[int]]): prompt_repl: _BoundPromptReplacement[_T] match: _TokenMatch @property def start_idx(self) -> int: return self.match.start_idx @property def end_idx(self) -> int: return self.match.end_idx @property def repl_unit(self) -> list[int]: return self.prompt_repl.repl_unit.token_ids @dataclass(repr=False) class _PromptReplacementTextMatch(_PromptReplacementMatch[_T, str]): prompt_repl: _BoundPromptReplacement[_T] match: re.Match[str] @property def start_idx(self) -> int: return self.match.start() @property def end_idx(self) -> int: return self.match.end() @property def repl_unit(self) -> str: return self.prompt_repl.repl_unit.text class _PlaceholderInfo(NamedTuple): modality: str start_idx: int unit: list[int] unit_count: int @property def length(self) -> int: return len(self.unit) * self.unit_count def to_range(self) -> PlaceholderRange: return PlaceholderRange( offset=self.start_idx, length=self.length, ) def find_token_matches( prompt: list[int], prompt_repls: Sequence[_BoundPromptReplacement[_T]], ) -> list[_PromptReplacementTokenMatch[_T]]: """Return each target of :code:`prompt_repls` found in :code:`prompt`.""" return [ _PromptReplacementTokenMatch(prompt_repl, match) for prompt_repl in prompt_repls for match in iter_token_matches(prompt, prompt_repl.target.token_ids) ] def find_text_matches( prompt: str, prompt_repls: Sequence[_BoundPromptReplacement[_T]], ) -> list[_PromptReplacementTextMatch[_T]]: """Return each target of :code:`prompt_repls` found in :code:`prompt`.""" return [ _PromptReplacementTextMatch(prompt_repl, match) for prompt_repl in prompt_repls for match in re.finditer(re.escape(prompt_repl.target.text), prompt) ] def _resolve_matches( prompt: _S, matches: Sequence[_PromptReplacementMatch[_T, _S]], ) -> list[_PromptReplacementMatch[_T, _S]]: """ Resolve :code:`matches` to ensure that there are no overlapping matches, and sort them such that earlier matches take priority over later ones. """ seen_matches: list[Optional[_PromptReplacementMatch[_T, _S]]] \ = [None] * len(prompt) for match in matches: for idx in range(match.start_idx, match.end_idx): if seen_matches[idx] is not None: raise ValueError("Found overlapping matches " f"({seen_matches[idx]} and {match}) " f"at index={idx} of prompt={prompt}") seen_matches[idx] = match return sorted(matches, key=lambda x: x.start_idx) def _replace_matches( prompt: _S, matches: Sequence[_PromptReplacementMatch[_T, _S]], mm_items_by_modality: Mapping[str, list[_T]], hf_inputs: BatchFeature, ) -> list[_S]: out_seqs = list[_S]() prev_end_idx = 0 next_idx_by_modality = {modality: 0 for modality in mm_items_by_modality} for match in _resolve_matches(prompt, matches): modality = match.modality mm_items = mm_items_by_modality[modality] item_idx = next_idx_by_modality[modality] if item_idx >= len(mm_items): continue start_idx = match.start_idx end_idx = match.end_idx repl_unit = match.repl_unit repl_info = match.prompt_repl repl_count = repl_info.get_count(mm_items, hf_inputs, item_idx) out_seqs.append(prompt[prev_end_idx:start_idx] + repl_unit * repl_count) prev_end_idx = end_idx next_idx_by_modality[modality] += 1 out_seqs.append(prompt[prev_end_idx:]) return out_seqs def replace_token_matches( prompt: list[int], matches: Sequence[_PromptReplacementMatch[_T, list[int]]], mm_items_by_modality: Mapping[str, list[_T]], hf_inputs: BatchFeature, ) -> list[int]: """Apply :code:`prompt_repls` to :code:`prompt`.""" if not matches: return prompt token_id_seqs = _replace_matches( prompt, matches, mm_items_by_modality, hf_inputs, ) return flatten_2d_lists(token_id_seqs) def replace_text_matches( prompt: str, matches: Sequence[_PromptReplacementMatch[_T, str]], mm_items_by_modality: Mapping[str, list[_T]], hf_inputs: BatchFeature, ) -> str: """Apply :code:`prompt_repls` to :code:`prompt`.""" if not matches: return prompt texts = _replace_matches( prompt, matches, mm_items_by_modality, hf_inputs, ) return "".join(texts) def _merge_placeholder_matches( matches: Iterable[_PromptReplacementTokenMatch], ) -> Iterable[_PromptReplacementTokenMatch]: current_match = None for match in sorted(matches, key=lambda x: x.start_idx): if current_match is None: current_match = match elif (current_match.prompt_repl == match.prompt_repl and current_match.end_idx == match.start_idx): current_match = _PromptReplacementTokenMatch( current_match.prompt_repl, match=_TokenMatch(current_match.start_idx, match.end_idx), ) else: yield current_match current_match = match if current_match is not None: yield current_match def iter_placeholders( prompt_repls: Sequence[_BoundPromptReplacement[Any]], prompt: list[int], *, min_unit_count: int = 1, ) -> Iterable[_PlaceholderInfo]: """Yield each set of placeholder tokens found in :code:`token_ids`.""" if min_unit_count <= 0: raise ValueError("`min_unit_count` must be a positive integer") matches = (_PromptReplacementTokenMatch(prompt_repl, match) for prompt_repl in prompt_repls if len(repl_unit := prompt_repl.repl_unit.token_ids) > 0 for match in iter_token_matches(prompt, repl_unit)) for match in _merge_placeholder_matches(matches): unit = match.repl_unit placeholder = _PlaceholderInfo( modality=match.modality, start_idx=match.start_idx, unit=unit, unit_count=(match.end_idx - match.start_idx) // len(unit), ) if placeholder.unit_count >= min_unit_count: yield placeholder class MultiModalProcessor(ABC): """ Helper class to process multi-modal inputs to be used in vLLM. """ def __init__( self, ctx: InputProcessingContext, metadata: MultiModalProcessingMetadata, ) -> None: super().__init__() self.ctx = ctx self.metadata = metadata def _get_hf_processor(self) -> ProcessorMixin: return self.ctx.get_hf_processor() def _get_tokenizer(self) -> AnyTokenizer: return self.ctx.tokenizer def __call__( self, prompt: str, mm_data: MultiModalDataDict, mm_processor_kwargs: Mapping[str, object], ) -> MultiModalInputsV2: return self.apply(prompt, mm_data, mm_processor_kwargs) def _find_placeholders( self, all_prompt_repls: Sequence[_BoundPromptReplacement[Any]], new_token_ids: list[int], *, # To avoid false positives from multi-input when detecting # whether placeholder tokens have been inserted, in case # the target sequence is a subset of the replacement tokens min_unit_count: int = 16, ) -> list[_PlaceholderInfo]: return list( iter_placeholders( all_prompt_repls, new_token_ids, min_unit_count=min_unit_count, )) def _apply_hf_processor( self, prompt: str, mm_data: MultiModalDataDict, mm_processor_kwargs: Mapping[str, object], ) -> BatchFeature: hf_processor = self._get_hf_processor() processor_data = dict[str, Any]() passthrough_data = dict[str, Any]() for k, v in mm_data.items(): # TODO: Make a separate modality for embedding inputs # to avoid confusion if k in ("image", "video", "audio"): if isinstance(v, torch.Tensor) and v.ndim == 3: # Pass through embedding inputs (single) passthrough_data[f"{k}_embeds"] = [v] elif is_list_of(v, torch.Tensor) and v[0].ndim == 2: # Pass through embedding inputs (multi) passthrough_data[f"{k}_embeds"] = v else: # Map keys to plural form, e.g.: image -> images processor_data[f"{k}s"] = v else: processor_data[k] = v try: hf_inputs = hf_processor( text=prompt, # type: ignore **processor_data, **mm_processor_kwargs, return_tensors="pt", ) except Exception as exc: data = dict(text=prompt, **processor_data) raise RuntimeError( f"Failed to apply {type(hf_processor).__name__} " f"on data={data} with kwargs={mm_processor_kwargs}") from exc hf_inputs.update(passthrough_data) return hf_inputs def _bind_prompt_replacements( self, mm_data: MultiModalDataDict, ) -> list[_BoundPromptReplacement[Any]]: tokenizer = self._get_tokenizer() return [ prompt_repl.bind(modality, tokenizer) for modality, metadata in self.metadata.items() if modality in mm_data for prompt_repl in metadata.prompt_repls ] def _apply_prompt_replacements( self, mm_data: MultiModalDataDict, hf_inputs: BatchFeature, token_ids: list[int], prompt_repls: Sequence[_BoundPromptReplacement[Any]], ) -> tuple[list[int], str, list[_PlaceholderInfo]]: tokenizer = self._get_tokenizer() mm_items = to_multi_format(mm_data) token_matches = find_token_matches(token_ids, prompt_repls) # If the search text does not represent a special token, # it may have different token IDs in the prompt, because # the tokens may go across the boundaries of the search text. # ---- # e.g. when searching for "foo" in "food", if "food" itself makes # up a token, then the token ID of "foo" will not appear at all # ---- # Since it is inefficient to search for all possible tokenizations # of the search text in the prompt, we instead perform string # replacement on the decoded token IDs, then encode them back. if all( len(matches) >= len(mm_items[modality]) for modality, matches in full_groupby_modality(token_matches) ): # yapf: disable token_ids = replace_token_matches( token_ids, token_matches, mm_items, hf_inputs, ) text = _decode(tokenizer, token_ids) matched_repls = [match.prompt_repl for match in token_matches] else: text = _decode(tokenizer, token_ids) text_matches = find_text_matches(text, prompt_repls) text = replace_text_matches( text, text_matches, mm_items, hf_inputs, ) token_ids = _encode(tokenizer, text) matched_repls = [match.prompt_repl for match in text_matches] placeholders = self._find_placeholders(matched_repls, token_ids) return token_ids, text, placeholders def apply( self, prompt_text: str, mm_data: MultiModalDataDict, mm_processor_kwargs: Mapping[str, object], ) -> MultiModalInputsV2: """ Process multi-modal inputs to be used in vLLM. The main steps are: 1. Apply HF Processor on prompt text and multi-modal data together, outputting token IDs and processed tensors. 2. Find and replace sequences in the token IDs with placeholder tokens. The number of placeholder tokens equals the feature size of the multi-modal data outputted by the multi-modal encoder. 3. Extract information about the placeholder tokens from the processed token IDs. """ tokenizer = self._get_tokenizer() hf_inputs = self._apply_hf_processor(prompt_text, mm_data, mm_processor_kwargs) prompt_ids, = hf_inputs.pop("input_ids").tolist() mm_kwargs = MultiModalKwargs(hf_inputs) all_prompt_repls = self._bind_prompt_replacements(mm_data) # If HF processor already inserts placeholder tokens, # there is no need for us to insert them all_placeholders = self._find_placeholders(all_prompt_repls, prompt_ids) if all_placeholders: prompt_text = _decode(tokenizer, prompt_ids) else: ( prompt_ids, prompt_text, all_placeholders, ) = self._apply_prompt_replacements( mm_data, hf_inputs, prompt_ids, all_prompt_repls, ) mm_placeholders = { modality: [item.to_range() for item in items] for modality, items in full_groupby_modality(all_placeholders) } return MultiModalInputsV2( type="multimodal", prompt=prompt_text, prompt_token_ids=prompt_ids, mm_kwargs=mm_kwargs, mm_placeholders=mm_placeholders, ) @abstractmethod def _get_dummy_mm_kwargs( self, mm_counts: Mapping[str, int], ) -> MultiModalKwargs: """ Build the input that corresponds to `mm_max_tokens` in :meth:`get_dummy_data`. """ raise NotImplementedError def get_dummy_data( self, seq_len: int, mm_counts: Mapping[str, int], mm_max_tokens: Mapping[str, int], ) -> DummyData: # Avoid circular import from vllm.sequence import SequenceData tokenizer = self._get_tokenizer() mm_placeholders = dict[str, _PlaceholderInfo]() offset = 0 for modality, max_tokens in mm_max_tokens.items(): if max_tokens == 0: continue metadata = self.metadata[modality] repl = metadata.prompt_repls[0].bind(modality, tokenizer) repl_token_ids = repl.repl_unit.token_ids placeholders = _PlaceholderInfo( modality=modality, start_idx=offset, unit=repl_token_ids, unit_count=max_tokens // len(repl_token_ids), ) mm_placeholders[modality] = placeholders offset += placeholders.length prompt_token_ids = flatten_2d_lists( [p.unit * p.unit_count for p in mm_placeholders.values()]) prompt_token_ids.extend([0] * (seq_len - len(prompt_token_ids))) return DummyData( seq_data=SequenceData.from_seqs(prompt_token_ids), multi_modal_data=self._get_dummy_mm_kwargs(mm_counts), multi_modal_placeholders={ modality: [p.to_range()] for modality, p in mm_placeholders.items() }, )